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Auto-Scalable, Policy-Driven File Routing using AI and Google Cloud Native Services

by Raghava Chellu, Ravi Kiran Gadiraju
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 26
Year of Publication: 2025
Authors: Raghava Chellu, Ravi Kiran Gadiraju
10.5120/ijca2025925466

Raghava Chellu, Ravi Kiran Gadiraju . Auto-Scalable, Policy-Driven File Routing using AI and Google Cloud Native Services. International Journal of Computer Applications. 187, 26 ( Jul 2025), 43-49. DOI=10.5120/ijca2025925466

@article{ 10.5120/ijca2025925466,
author = { Raghava Chellu, Ravi Kiran Gadiraju },
title = { Auto-Scalable, Policy-Driven File Routing using AI and Google Cloud Native Services },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 26 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 43-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number26/auto-scalable-policy-driven-file-routing-using-ai-and-google-cloud-native-services/ },
doi = { 10.5120/ijca2025925466 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-31T02:40:11.783230+05:30
%A Raghava Chellu
%A Ravi Kiran Gadiraju
%T Auto-Scalable, Policy-Driven File Routing using AI and Google Cloud Native Services
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 26
%P 43-49
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study proposes an Artificial Intelligence (AI) based, scalable, and policy-driven file routing system that utilizes Cloud services through Google Cloud Native, leveraging the inefficiencies of conventional file movement frameworks. Using tools such as Google Cloud Storage, Eventarc, Pub/Sub, and Cloud Run in a Dockerized environment, the system enables the smart classification of files, dynamic policy analysis, and secure file transmission over various protocols. The quality of service packet routing optimizations is performed by using AI model functions to optimize routing decisions involving file metadata, network load, and security parameters, utilizing Random Forest, SVM, and Artificial Neural Networks. The architecture is auto-scaled with the help of Google Cloud’s serverless infrastructure, which maximizes resource efficiency and accountability to changes in the workload. The criteria of accuracy, latency, resource utilization, and scalability will demonstrate the effectiveness of the AI-driven method. The given solution is an effective alternative to older systems, offering increased performance, compliance, and operational efficiency that can be beneficial in contemporary, data-driven businesses.

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Index Terms

Computer Science
Information Sciences

Keywords

Google Cloud Storage Eventarc Pub/sub Cloud Run Managed File Transfer File Transmission Protocols Security Network bandwidth Docker container

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